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Robust scan registration for navigation in forest environment using low-resolution LiDAR sensors
Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.ORCID iD: 0000-0001-9364-7994
Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-2953-1564
Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden; Perception for Intelligent Systems, Technical University of Munich, Munich, Germany.ORCID iD: 0000-0003-0217-9326
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.ORCID iD: 0000-0003-4685-379X
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 10, article id 4736Article in journal (Refereed) Published
Abstract [en]

Automated forest machines are becoming important due to human operators’ complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps.

Place, publisher, year, edition, pages
MDPI, 2023. Vol. 23, no 10, article id 4736
Keywords [en]
tree segmentation, LiDAR mapping, forest inventory, SLAM, forestry robotics, scan registration
National Category
Robotics Forest Science
Research subject
Computer Science; English
Identifiers
URN: urn:nbn:se:umu:diva-208254DOI: 10.3390/s23104736ISI: 000997887900001Scopus ID: 2-s2.0-85160406537OAI: oai:DiVA.org:umu-208254DiVA, id: diva2:1756770
Funder
EU, Horizon 2020, 858101Available from: 2023-05-15 Created: 2023-05-15 Last updated: 2023-09-05Bibliographically approved

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Kurtser, Polina

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